Pharmachk: robust device for counterfeit and substandard medicines screening on developing regions

Thesis (Ph.D.)--Boston UniversityCounterfeit and substandard medicines are a grave public health concern that comprises a $75B black market and claims over 100,000 lives every year. The World Health Organization estimates that 10-50% of medicines in countries around the world are adulterated, and their presence imposes serious financial and economic burdens while also contributing to the rise of drug-resistant pathogens. Although a plethora of technologies are available for field-based quality screening, none reliably quantify active pharmaceutical ingredient (API) content or kinetic release from a dissolving tablet. The United States Pharmacopeia, a global leader in medicines standards for over 150 years, indicates that these quality measures are vitally important yet remain outside of the reach ofexisting screening tools. The current field standard relies on thin layer chromatography to only provide qualitative results that make it difficult to discern between tablets that contain 80% and 100% API. Meanwhile, international standards set the threshold for substandard medicines at 90%. This clear lack of appropriately quantitative and field- ready analytical tools poses a serious problem for national and international policymakers who are plagued with wildly variable information that prevents focused and deliberate action against the spread ofthese medications. This work presents an alternative analytical technique that can specifically and accurately quantify drug API content and kinetic release. PharmaChk provides an orthogonal approach to existing technologies using a portable, inexpensive, and easy-to-use platform. We demonstrate that aptamers can provide a simple and effective way to target a wide range of APis, while maintaining high quantitative precision and accuracy. A microfluidic, flow-through system is employed to obtain valuable drug quality information using a single step procedure. Through our research, we demonstrate the development of the PharmaChk platform from the proof-of-concept stage to beta prototyping and field-testing. By providing a portable, robust, and quantitative approach to medicines testing, PharmaChk can enable the collection of important drug quality information throughout pharmaceutical supply chains and ultimately save the lives of millions that are not afforded safe and essential medicines

Controlling uncertainty in aptamer selection

The search for high-affinity aptamers for targets such as proteins, small molecules, or cancer cells remains a formidable endeavor. Systematic Evolution of Ligands by EXponential Enrichment (SELEX) offers an iterative process to discover these aptamers through evolutionary selection of high-affinity candidates from a highly diverse random pool. This randomness dictates an unknown population distribution of fitness parameters, encoded by the binding affinities, toward SELEX targets. Adding to this uncertainty, repeating SELEX under identical conditions may lead to variable outcomes. These uncertainties pose a challenge when tuning selection pressures to isolate high-affinity ligands. Here, we present a stochastic hybrid model that describes the evolutionary selection of aptamers to explore the impact of these unknowns. To our surprise, we find that even single copies of high-affinity ligands in a pool of billions can strongly influence population dynamics, yet their survival is highly dependent on chance. We perform Monte Carlo simulations to explore the impact of environmental parameters, such as the target concentration, on selection efficiency in SELEX and identify strategies to control these uncertainties to ultimately improve the outcome and speed of this time- and resource-intensive process.National Cancer Institute (U.S.) (5U01CA177799

Effects of antibiotic interaction on antimicrobial resistance development in wastewater

Abstract While wastewater is understood to be a critically important reservoir of antimicrobial resistance due to the presence of multiple antibiotic residues from industrial and agricultural runoff, there is little known about the effects of antibiotic interactions in the wastewater on the development of resistance. We worked to fill this gap in quantitative understanding of antibiotic interaction in constant flow environments by experimentally monitoring E. coli populations under subinhibitory concentrations of combinations of antibiotics with synergistic, antagonistic, and additive interactions. We then used these results to expand our previously developed computational model to account for the effects of antibiotic interaction. We found that populations grown under synergistic and antagonistic antibiotic conditions exhibited significant differences from predicted behavior. E. coli populations grown with synergistically interacting antibiotics developed less resistance than predicted, indicating that synergistic antibiotics may have a suppressive effect on resistance development. Furthermore E. coli populations grown with antagonistically interacting antibiotics showed an antibiotic ratio-dependent development of resistance, suggesting that not only antibiotic interaction, but relative concentration is important in predicting resistance development. These results provide critical insight for quantitatively understanding the effects of antibiotic interactions in wastewater and provide a basis for future studies in modelling resistance in these environments

Modeling patient access to therapeutic oxytocin in Zanzibar, Tanzania

Abstract Background Our objective is to estimate the effects of therapeutic oxytocin supply chain factors and social determinants of health on patient access to oxytocin in low-income settings using system dynamics modeling. Postpartum hemorrhage (PPH), a major cause of maternal mortality disproportionately affects women in low and middle income countries (LMICs). The World Health Organization recommends therapeutic oxytocin as the frontline uterotonic for PPH management and prevention. However, lack of access to quality therapeutic oxytocin in Tanzania, and throughout Sub-Saharan Africa, continues to result in a high number of preventable maternal deaths. Methods We used publicly available data from Zanzibar and Sub-Saharan Africa, literature review, oxytocin degradation kinetics and previously developed systems dynamics models to understand the barriers in patient access to quality therapeutic oxytocin. Results The model makes four basic predictions. First, there is a major gap between therapeutic oxytocin procurement and availability. Second, it predicts that at current population increase rates, oxytocin supply will have to be doubled in the next 30 years. Third, supply and storage temperature until 30 °C has minimal effect on oxytocin quality and finally distance of 5 km or less to birthing facility has a small effect on overall access to oxytocin. Conclusions The model provides a systems level approach to therapeutic oxytocin access, incorporating supply and procurement, socio-economic factors, as well as storage conditions to understand how women’s access to oxytocin over time can be sustained for better health outcomes

Predicting resource-dependent maternal health outcomes at a referral hospital in Zanzibar using patient trajectories and mathematical modeling.

Poor intra-facility maternity care is a major contributor to maternal mortality in low- and middle-income countries. Close to 830 women die each day due to preventable maternal complications, partly due to the increasing number of women giving birth in health facilities that are not adequately resourced to manage growing patient populations. Barriers to adequate care during the 'last mile' of healthcare delivery are attributable to deficiencies at multiple levels: education, staff, medication, facilities, and delays in receiving care. Moreover, the scope and multi-scale interdependence of these factors make individual contributions of each challenging to analyze, particularly in settings where basic data registration is often lacking. To address this need, we have designed and implemented a novel systems-level and dynamic mathematical model that simulates the impact of hospital resource allocations on maternal mortality rates at Mnazi Mmoja Hospital (MMH), a referral hospital in Zanzibar, Tanzania. The purpose of this model is to provide a rigorous and flexible tool that enables hospital administrators and public health officials to quantitatively analyze the impact of resource constraints on patient outcomes within the maternity ward, and prioritize key areas for further human or capital investment. Currently, no such tool exists to assist administrators and policy makers with effective resource allocation and planning. This paper describes the structure and construct of the model, provides validation of the assumptions made with anonymized patient data and discusses the predictive capacity of our model. Application of the model to specific resource allocations, maternal treatment plans, and hospital loads at MMH indicates through quantitative results that medicine stocking schedules and staff allocations are key areas that can be addressed to reduce mortality by up to 5-fold. With data-driven evidence provided by the model, hospital staff, administration, and the local ministries of health can enact policy changes and implement targeted interventions to improve maternal health outcomes at MMH. While our model is able to determine specific gaps in resources and health care delivery specifically at MMH, the model should be viewed as an additional tool that may be used by other facilities seeking to analyze and improve maternal health outcomes in resource constrained environments